Tongue Droplet Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Tongue"Load the requisite packages and some additional helper functions.
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')
validate_cell_ontology = function(cell_ontology_class){
in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
if (!all(in_cell_ontology)) {
message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
stop(message)
}
}
convert_to_cell_ontology_id = function(cell_ontology_class){
return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}
save_dir = here('00_data_ingest', '04_tissue_robj_generated')# read the metadata to get the plates we want
droplet_metadata_filename = here('00_data_ingest', '01_droplet_raw_data', 'metadata_droplet.csv')
droplet_metadata <- read.csv(droplet_metadata_filename, sep=",", header = TRUE)
colnames(droplet_metadata)[1] <- "channel"
droplet_metadataSubset the metadata on the tissue.
tissue_metadata = filter(droplet_metadata, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex')]
tissue_metadataUse only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to “expand” the per-plate metadata to be per-cell.
# Load the gene names and set the metadata columns by opening the first file
subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[1])
raw.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata$channel[1], '_', x))
meta.data = data.frame(row.names = colnames(raw.data))
meta.data['channel'] = tissue_metadata$channel[1]
if (length(tissue_metadata$channel) > 1){
# Some tissues, like Thymus and Heart had only one channel
for(i in 2:nrow(tissue_metadata)){
subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[i])
new.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(new.data) <- lapply(colnames(new.data), function(x) paste0(tissue_metadata$channel[i], '_', x))
new.metadata = data.frame(row.names = colnames(new.data))
new.metadata['channel'] = tissue_metadata$channel[i]
raw.data = cbind(raw.data, new.data)
meta.data = rbind(meta.data, new.metadata)
}
}
rnames = row.names(meta.data)
meta.data <- merge(meta.data, tissue_metadata, sort = F)
row.names(meta.data) <- rnames
dim(raw.data)[1] 23433 7538corner(raw.data)[1] 0 0 0 2 0head(meta.data)Process the raw data and load it into the Seurat object.
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]
# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest,
min.cells = 5, min.genes = 5)
tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
# colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'
# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NACalculate percent ribosomal genes.
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")A sanity check: genes per cell vs reads per cell.
GenePlot(object = tiss, gene1 = "nUMI", gene2 = "nGene", use.raw=T)
Filter out cells with few reads and few genes.
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nUMI"),
low.thresholds = c(500, 1000), high.thresholds = c(25000, 5000000))Normalize the data, then regress out correlation with total reads
tiss <- NormalizeData(object = tiss, scale.factor = 1e4)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|tiss <- ScaleData(object = tiss)[1] "Scaling data matrix"
|
| | 0%
|
|========= | 7%
|
|================= | 13%
|
|========================== | 20%
|
|================================== | 27%
|
|=========================================== | 33%
|
|=================================================== | 40%
|
|============================================================ | 47%
|
|==================================================================== | 53%
|
|============================================================================= | 60%
|
|===================================================================================== | 67%
|
|============================================================================================== | 73%
|
|====================================================================================================== | 80%
|
|=============================================================================================================== | 87%
|
|======================================================================================================================= | 93%
|
|================================================================================================================================| 100%tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)Calculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Run Principal Component Analysis.
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 14The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=35, dim.embed = 2)
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/Jintonic/Documents/GitHub/tabula-muris/00_data_ingest/04_tissue_robj_generated/dropletTongue_seurat_tiss.Robj"save(tiss, file=filename)Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9 10
1653 1377 853 780 698 589 489 471 389 202 37 tiss1=BuildClusterTree(tiss)[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
[1] "Finished averaging RNA for cluster 8"
[1] "Finished averaging RNA for cluster 9"
[1] "Finished averaging RNA for cluster 10"
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 2 % ~20s
|++ | 4 % ~19s
|+++ | 6 % ~18s
|++++ | 8 % ~18s
|+++++ | 10% ~17s
|++++++ | 12% ~17s
|+++++++ | 14% ~16s
|++++++++ | 16% ~16s
|+++++++++ | 18% ~16s
|++++++++++ | 20% ~19s
|+++++++++++ | 22% ~18s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 29% ~16s
|++++++++++++++++ | 31% ~15s
|+++++++++++++++++ | 33% ~14s
|++++++++++++++++++ | 35% ~14s
|+++++++++++++++++++ | 37% ~13s
|++++++++++++++++++++ | 39% ~13s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 45% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~10s
|+++++++++++++++++++++++++++ | 53% ~10s
|++++++++++++++++++++++++++++ | 55% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|++++++++++++++++++++++++++++++ | 59% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 20s#cluster7.markers <- FindMarkers(object = tiss, ident.1 = 7, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster8.markers <- FindMarkers(object = tiss, ident.1 = 8, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster9.markers <- FindMarkers(object = tiss, ident.1 = 9, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)Which markers identify a specific cluster?
print(x = head(x = cluster6.markers, n = 30))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~01m 08s
|++ | 2 % ~01m 05s
|++ | 3 % ~01m 05s
|+++ | 4 % ~01m 05s
|+++ | 6 % ~01m 04s
|++++ | 7 % ~01m 04s
|++++ | 8 % ~01m 03s
|+++++ | 9 % ~01m 02s
|++++++ | 10% ~01m 01s
|++++++ | 11% ~01m 01s
|+++++++ | 12% ~60s
|+++++++ | 13% ~59s
|++++++++ | 15% ~58s
|++++++++ | 16% ~57s
|+++++++++ | 17% ~56s
|+++++++++ | 18% ~56s
|++++++++++ | 19% ~55s
|+++++++++++ | 20% ~54s
|+++++++++++ | 21% ~53s
|++++++++++++ | 22% ~53s
|++++++++++++ | 24% ~52s
|+++++++++++++ | 25% ~51s
|+++++++++++++ | 26% ~51s
|++++++++++++++ | 27% ~50s
|+++++++++++++++ | 28% ~49s
|+++++++++++++++ | 29% ~48s
|++++++++++++++++ | 30% ~48s
|++++++++++++++++ | 31% ~48s
|+++++++++++++++++ | 33% ~47s
|+++++++++++++++++ | 34% ~46s
|++++++++++++++++++ | 35% ~46s
|++++++++++++++++++ | 36% ~45s
|+++++++++++++++++++ | 37% ~44s
|++++++++++++++++++++ | 38% ~43s
|++++++++++++++++++++ | 39% ~42s
|+++++++++++++++++++++ | 40% ~41s
|+++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~40s
|++++++++++++++++++++++ | 44% ~39s
|+++++++++++++++++++++++ | 45% ~38s
|++++++++++++++++++++++++ | 46% ~37s
|++++++++++++++++++++++++ | 47% ~36s
|+++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~35s
|++++++++++++++++++++++++++ | 51% ~34s
|++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~33s
|+++++++++++++++++++++++++++ | 54% ~32s
|++++++++++++++++++++++++++++ | 55% ~31s
|+++++++++++++++++++++++++++++ | 56% ~30s
|+++++++++++++++++++++++++++++ | 57% ~29s
|++++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|+++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 63% ~26s
|+++++++++++++++++++++++++++++++++ | 64% ~25s
|+++++++++++++++++++++++++++++++++ | 65% ~24s
|++++++++++++++++++++++++++++++++++ | 66% ~23s
|++++++++++++++++++++++++++++++++++ | 67% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|++++++++++++++++++++++++++++++++++++ | 72% ~19s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|++++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 09s
| | 0 % ~calculating
|+ | 1 % ~01m 17s
|++ | 2 % ~01m 14s
|++ | 3 % ~01m 12s
|+++ | 4 % ~01m 12s
|+++ | 5 % ~01m 11s
|++++ | 6 % ~01m 11s
|++++ | 7 % ~01m 10s
|+++++ | 8 % ~01m 09s
|+++++ | 9 % ~01m 09s
|++++++ | 10% ~01m 08s
|++++++ | 11% ~01m 07s
|+++++++ | 12% ~01m 06s
|+++++++ | 13% ~01m 06s
|++++++++ | 14% ~01m 05s
|++++++++ | 15% ~01m 04s
|+++++++++ | 16% ~01m 03s
|+++++++++ | 17% ~01m 02s
|++++++++++ | 18% ~01m 09s
|++++++++++ | 19% ~01m 08s
|+++++++++++ | 20% ~01m 07s
|+++++++++++ | 21% ~01m 06s
|++++++++++++ | 22% ~01m 05s
|++++++++++++ | 23% ~01m 03s
|+++++++++++++ | 24% ~01m 02s
|+++++++++++++ | 25% ~01m 01s
|++++++++++++++ | 26% ~01m 00s
|++++++++++++++ | 27% ~59s
|+++++++++++++++ | 28% ~58s
|+++++++++++++++ | 29% ~57s
|++++++++++++++++ | 30% ~56s
|++++++++++++++++ | 31% ~55s
|+++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|++++++++++++++++++ | 34% ~52s
|++++++++++++++++++ | 35% ~51s
|+++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|++++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~48s
|+++++++++++++++++++++ | 40% ~47s
|+++++++++++++++++++++ | 41% ~46s
|++++++++++++++++++++++ | 42% ~45s
|++++++++++++++++++++++ | 43% ~44s
|+++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~43s
|++++++++++++++++++++++++ | 46% ~42s
|++++++++++++++++++++++++ | 47% ~41s
|+++++++++++++++++++++++++ | 48% ~40s
|+++++++++++++++++++++++++ | 49% ~39s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~37s
|+++++++++++++++++++++++++++ | 54% ~36s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~34s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 16s
| | 0 % ~calculating
|+ | 1 % ~02m 08s
|++ | 2 % ~02m 07s
|++ | 3 % ~02m 04s
|+++ | 4 % ~02m 02s
|+++ | 5 % ~02m 01s
|++++ | 6 % ~01m 60s
|++++ | 7 % ~02m 04s
|+++++ | 8 % ~02m 01s
|+++++ | 9 % ~01m 59s
|++++++ | 10% ~01m 57s
|++++++ | 11% ~01m 56s
|+++++++ | 12% ~01m 54s
|+++++++ | 14% ~01m 52s
|++++++++ | 15% ~01m 50s
|++++++++ | 16% ~01m 49s
|+++++++++ | 17% ~01m 47s
|+++++++++ | 18% ~01m 46s
|++++++++++ | 19% ~01m 45s
|++++++++++ | 20% ~01m 44s
|+++++++++++ | 21% ~01m 42s
|+++++++++++ | 22% ~01m 41s
|++++++++++++ | 23% ~01m 39s
|++++++++++++ | 24% ~01m 38s
|+++++++++++++ | 25% ~01m 36s
|++++++++++++++ | 26% ~01m 35s
|++++++++++++++ | 27% ~01m 33s
|+++++++++++++++ | 28% ~01m 32s
|+++++++++++++++ | 29% ~01m 31s
|++++++++++++++++ | 30% ~01m 29s
|++++++++++++++++ | 31% ~01m 28s
|+++++++++++++++++ | 32% ~01m 27s
|+++++++++++++++++ | 33% ~01m 25s
|++++++++++++++++++ | 34% ~01m 24s
|++++++++++++++++++ | 35% ~01m 23s
|+++++++++++++++++++ | 36% ~01m 21s
|+++++++++++++++++++ | 38% ~01m 20s
|++++++++++++++++++++ | 39% ~01m 18s
|++++++++++++++++++++ | 40% ~01m 17s
|+++++++++++++++++++++ | 41% ~01m 16s
|+++++++++++++++++++++ | 42% ~01m 15s
|++++++++++++++++++++++ | 43% ~01m 13s
|++++++++++++++++++++++ | 44% ~01m 12s
|+++++++++++++++++++++++ | 45% ~01m 11s
|+++++++++++++++++++++++ | 46% ~01m 10s
|++++++++++++++++++++++++ | 47% ~01m 09s
|++++++++++++++++++++++++ | 48% ~01m 07s
|+++++++++++++++++++++++++ | 49% ~01m 06s
|+++++++++++++++++++++++++ | 50% ~01m 05s
|++++++++++++++++++++++++++ | 51% ~01m 03s
|+++++++++++++++++++++++++++ | 52% ~01m 02s
|+++++++++++++++++++++++++++ | 53% ~01m 01s
|++++++++++++++++++++++++++++ | 54% ~59s
|++++++++++++++++++++++++++++ | 55% ~58s
|+++++++++++++++++++++++++++++ | 56% ~56s
|+++++++++++++++++++++++++++++ | 57% ~55s
|++++++++++++++++++++++++++++++ | 58% ~54s
|++++++++++++++++++++++++++++++ | 59% ~52s
|+++++++++++++++++++++++++++++++ | 60% ~51s
|+++++++++++++++++++++++++++++++ | 61% ~50s
|++++++++++++++++++++++++++++++++ | 62% ~48s
|++++++++++++++++++++++++++++++++ | 64% ~47s
|+++++++++++++++++++++++++++++++++ | 65% ~46s
|+++++++++++++++++++++++++++++++++ | 66% ~44s
|++++++++++++++++++++++++++++++++++ | 67% ~43s
|++++++++++++++++++++++++++++++++++ | 68% ~42s
|+++++++++++++++++++++++++++++++++++ | 69% ~40s
|+++++++++++++++++++++++++++++++++++ | 70% ~39s
|++++++++++++++++++++++++++++++++++++ | 71% ~38s
|++++++++++++++++++++++++++++++++++++ | 72% ~36s
|+++++++++++++++++++++++++++++++++++++ | 73% ~35s
|+++++++++++++++++++++++++++++++++++++ | 74% ~34s
|++++++++++++++++++++++++++++++++++++++ | 75% ~32s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~31s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~28s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~26s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~24s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~23s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~19s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 10s
| | 0 % ~calculating
|+ | 1 % ~02m 46s
|++ | 2 % ~02m 44s
|++ | 3 % ~02m 41s
|+++ | 4 % ~02m 40s
|+++ | 5 % ~02m 38s
|++++ | 6 % ~02m 36s
|++++ | 7 % ~02m 35s
|+++++ | 8 % ~02m 34s
|+++++ | 9 % ~02m 33s
|++++++ | 10% ~02m 31s
|++++++ | 11% ~02m 30s
|+++++++ | 12% ~02m 28s
|+++++++ | 14% ~02m 29s
|++++++++ | 15% ~02m 27s
|++++++++ | 16% ~02m 25s
|+++++++++ | 17% ~02m 23s
|+++++++++ | 18% ~02m 24s
|++++++++++ | 19% ~02m 22s
|++++++++++ | 20% ~02m 20s
|+++++++++++ | 21% ~02m 17s
|+++++++++++ | 22% ~02m 16s
|++++++++++++ | 23% ~02m 13s
|++++++++++++ | 24% ~02m 11s
|+++++++++++++ | 25% ~02m 09s
|++++++++++++++ | 26% ~02m 07s
|++++++++++++++ | 27% ~02m 05s
|+++++++++++++++ | 28% ~02m 03s
|+++++++++++++++ | 29% ~02m 01s
|++++++++++++++++ | 30% ~01m 59s
|++++++++++++++++ | 31% ~01m 58s
|+++++++++++++++++ | 32% ~01m 56s
|+++++++++++++++++ | 33% ~01m 54s
|++++++++++++++++++ | 34% ~01m 52s
|++++++++++++++++++ | 35% ~01m 51s
|+++++++++++++++++++ | 36% ~01m 49s
|+++++++++++++++++++ | 38% ~01m 47s
|++++++++++++++++++++ | 39% ~01m 45s
|++++++++++++++++++++ | 40% ~01m 43s
|+++++++++++++++++++++ | 41% ~01m 41s
|+++++++++++++++++++++ | 42% ~01m 40s
|++++++++++++++++++++++ | 43% ~01m 38s
|++++++++++++++++++++++ | 44% ~01m 36s
|+++++++++++++++++++++++ | 45% ~01m 34s
|+++++++++++++++++++++++ | 46% ~01m 32s
|++++++++++++++++++++++++ | 47% ~01m 30s
|++++++++++++++++++++++++ | 48% ~01m 29s
|+++++++++++++++++++++++++ | 49% ~01m 27s
|+++++++++++++++++++++++++ | 50% ~01m 25s
|++++++++++++++++++++++++++ | 51% ~01m 23s
|+++++++++++++++++++++++++++ | 52% ~01m 22s
|+++++++++++++++++++++++++++ | 53% ~01m 20s
|++++++++++++++++++++++++++++ | 54% ~01m 18s
|++++++++++++++++++++++++++++ | 55% ~01m 16s
|+++++++++++++++++++++++++++++ | 56% ~01m 14s
|+++++++++++++++++++++++++++++ | 57% ~01m 13s
|++++++++++++++++++++++++++++++ | 58% ~01m 12s
|++++++++++++++++++++++++++++++ | 59% ~01m 10s
|+++++++++++++++++++++++++++++++ | 60% ~01m 08s
|+++++++++++++++++++++++++++++++ | 61% ~01m 06s
|++++++++++++++++++++++++++++++++ | 62% ~01m 04s
|++++++++++++++++++++++++++++++++ | 64% ~01m 02s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 01s
|+++++++++++++++++++++++++++++++++ | 66% ~59s
|++++++++++++++++++++++++++++++++++ | 67% ~57s
|++++++++++++++++++++++++++++++++++ | 68% ~55s
|+++++++++++++++++++++++++++++++++++ | 69% ~54s
|+++++++++++++++++++++++++++++++++++ | 70% ~52s
|++++++++++++++++++++++++++++++++++++ | 71% ~50s
|++++++++++++++++++++++++++++++++++++ | 72% ~49s
|+++++++++++++++++++++++++++++++++++++ | 73% ~47s
|+++++++++++++++++++++++++++++++++++++ | 74% ~45s
|++++++++++++++++++++++++++++++++++++++ | 75% ~43s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~41s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~40s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~38s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~36s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~34s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~32s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~31s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~29s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~27s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~25s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~23s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~22s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~18s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 52s
| | 0 % ~calculating
|+ | 1 % ~01m 20s
|++ | 2 % ~01m 18s
|++ | 3 % ~01m 16s
|+++ | 5 % ~01m 16s
|+++ | 6 % ~01m 15s
|++++ | 7 % ~01m 14s
|+++++ | 8 % ~01m 14s
|+++++ | 9 % ~01m 13s
|++++++ | 10% ~01m 12s
|++++++ | 12% ~01m 11s
|+++++++ | 13% ~01m 10s
|+++++++ | 14% ~01m 09s
|++++++++ | 15% ~01m 08s
|+++++++++ | 16% ~01m 07s
|+++++++++ | 17% ~01m 06s
|++++++++++ | 19% ~01m 05s
|++++++++++ | 20% ~01m 04s
|+++++++++++ | 21% ~01m 05s
|++++++++++++ | 22% ~01m 05s
|++++++++++++ | 23% ~01m 04s
|+++++++++++++ | 24% ~01m 03s
|+++++++++++++ | 26% ~01m 02s
|++++++++++++++ | 27% ~01m 01s
|++++++++++++++ | 28% ~01m 00s
|+++++++++++++++ | 29% ~59s
|++++++++++++++++ | 30% ~58s
|++++++++++++++++ | 31% ~57s
|+++++++++++++++++ | 33% ~56s
|+++++++++++++++++ | 34% ~55s
|++++++++++++++++++ | 35% ~54s
|+++++++++++++++++++ | 36% ~53s
|+++++++++++++++++++ | 37% ~52s
|++++++++++++++++++++ | 38% ~51s
|++++++++++++++++++++ | 40% ~50s
|+++++++++++++++++++++ | 41% ~49s
|+++++++++++++++++++++ | 42% ~48s
|++++++++++++++++++++++ | 43% ~47s
|+++++++++++++++++++++++ | 44% ~46s
|+++++++++++++++++++++++ | 45% ~45s
|++++++++++++++++++++++++ | 47% ~44s
|++++++++++++++++++++++++ | 48% ~43s
|+++++++++++++++++++++++++ | 49% ~42s
|+++++++++++++++++++++++++ | 50% ~41s
|++++++++++++++++++++++++++ | 51% ~40s
|+++++++++++++++++++++++++++ | 52% ~39s
|+++++++++++++++++++++++++++ | 53% ~38s
|++++++++++++++++++++++++++++ | 55% ~38s
|++++++++++++++++++++++++++++ | 56% ~37s
|+++++++++++++++++++++++++++++ | 57% ~36s
|++++++++++++++++++++++++++++++ | 58% ~35s
|++++++++++++++++++++++++++++++ | 59% ~34s
|+++++++++++++++++++++++++++++++ | 60% ~33s
|+++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~31s
|++++++++++++++++++++++++++++++++ | 64% ~30s
|+++++++++++++++++++++++++++++++++ | 65% ~29s
|++++++++++++++++++++++++++++++++++ | 66% ~28s
|++++++++++++++++++++++++++++++++++ | 67% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|+++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~24s
|+++++++++++++++++++++++++++++++++++++ | 72% ~23s
|+++++++++++++++++++++++++++++++++++++ | 73% ~22s
|++++++++++++++++++++++++++++++++++++++ | 74% ~21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 25s
| | 0 % ~calculating
|+ | 1 % ~02m 15s
|++ | 2 % ~02m 14s
|++ | 3 % ~02m 12s
|+++ | 4 % ~02m 11s
|+++ | 5 % ~02m 10s
|++++ | 7 % ~02m 08s
|++++ | 8 % ~02m 07s
|+++++ | 9 % ~02m 06s
|+++++ | 10% ~02m 04s
|++++++ | 11% ~02m 03s
|++++++ | 12% ~02m 01s
|+++++++ | 13% ~01m 60s
|++++++++ | 14% ~01m 59s
|++++++++ | 15% ~01m 57s
|+++++++++ | 16% ~01m 56s
|+++++++++ | 17% ~01m 54s
|++++++++++ | 18% ~01m 53s
|++++++++++ | 20% ~01m 51s
|+++++++++++ | 21% ~01m 50s
|+++++++++++ | 22% ~01m 48s
|++++++++++++ | 23% ~01m 47s
|++++++++++++ | 24% ~01m 45s
|+++++++++++++ | 25% ~01m 44s
|++++++++++++++ | 26% ~01m 42s
|++++++++++++++ | 27% ~01m 41s
|+++++++++++++++ | 28% ~01m 39s
|+++++++++++++++ | 29% ~01m 38s
|++++++++++++++++ | 30% ~01m 36s
|++++++++++++++++ | 32% ~01m 35s
|+++++++++++++++++ | 33% ~01m 33s
|+++++++++++++++++ | 34% ~01m 32s
|++++++++++++++++++ | 35% ~01m 30s
|++++++++++++++++++ | 36% ~01m 29s
|+++++++++++++++++++ | 37% ~01m 27s
|++++++++++++++++++++ | 38% ~01m 26s
|++++++++++++++++++++ | 39% ~01m 24s
|+++++++++++++++++++++ | 40% ~01m 23s
|+++++++++++++++++++++ | 41% ~01m 21s
|++++++++++++++++++++++ | 42% ~01m 20s
|++++++++++++++++++++++ | 43% ~01m 18s
|+++++++++++++++++++++++ | 45% ~01m 17s
|+++++++++++++++++++++++ | 46% ~01m 15s
|++++++++++++++++++++++++ | 47% ~01m 15s
|++++++++++++++++++++++++ | 48% ~01m 13s
|+++++++++++++++++++++++++ | 49% ~01m 12s
|+++++++++++++++++++++++++ | 50% ~01m 10s
|++++++++++++++++++++++++++ | 51% ~01m 08s
|+++++++++++++++++++++++++++ | 52% ~01m 07s
|+++++++++++++++++++++++++++ | 53% ~01m 05s
|++++++++++++++++++++++++++++ | 54% ~01m 04s
|++++++++++++++++++++++++++++ | 55% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|+++++++++++++++++++++++++++++ | 58% ~59s
|++++++++++++++++++++++++++++++ | 59% ~58s
|++++++++++++++++++++++++++++++ | 60% ~56s
|+++++++++++++++++++++++++++++++ | 61% ~55s
|+++++++++++++++++++++++++++++++ | 62% ~53s
|++++++++++++++++++++++++++++++++ | 63% ~52s
|+++++++++++++++++++++++++++++++++ | 64% ~50s
|+++++++++++++++++++++++++++++++++ | 65% ~49s
|++++++++++++++++++++++++++++++++++ | 66% ~47s
|++++++++++++++++++++++++++++++++++ | 67% ~46s
|+++++++++++++++++++++++++++++++++++ | 68% ~44s
|+++++++++++++++++++++++++++++++++++ | 70% ~43s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|++++++++++++++++++++++++++++++++++++ | 72% ~40s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|+++++++++++++++++++++++++++++++++++++ | 74% ~37s
|++++++++++++++++++++++++++++++++++++++ | 75% ~35s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~34s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~31s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~29s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~28s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~26s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 20s
| | 0 % ~calculating
|+ | 1 % ~57s
|++ | 3 % ~57s
|++ | 4 % ~56s
|+++ | 5 % ~56s
|++++ | 6 % ~55s
|++++ | 8 % ~54s
|+++++ | 9 % ~53s
|++++++ | 10% ~53s
|++++++ | 11% ~52s
|+++++++ | 13% ~51s
|+++++++ | 14% ~51s
|++++++++ | 15% ~50s
|+++++++++ | 16% ~49s
|+++++++++ | 18% ~48s
|++++++++++ | 19% ~48s
|+++++++++++ | 20% ~47s
|+++++++++++ | 22% ~46s
|++++++++++++ | 23% ~45s
|+++++++++++++ | 24% ~44s
|+++++++++++++ | 25% ~44s
|++++++++++++++ | 27% ~43s
|++++++++++++++ | 28% ~42s
|+++++++++++++++ | 29% ~42s
|++++++++++++++++ | 30% ~41s
|++++++++++++++++ | 32% ~40s
|+++++++++++++++++ | 33% ~39s
|++++++++++++++++++ | 34% ~39s
|++++++++++++++++++ | 35% ~38s
|+++++++++++++++++++ | 37% ~37s
|+++++++++++++++++++ | 38% ~36s
|++++++++++++++++++++ | 39% ~36s
|+++++++++++++++++++++ | 41% ~35s
|+++++++++++++++++++++ | 42% ~34s
|++++++++++++++++++++++ | 43% ~33s
|+++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 46% ~32s
|++++++++++++++++++++++++ | 47% ~31s
|+++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~30s
|++++++++++++++++++++++++++ | 51% ~29s
|++++++++++++++++++++++++++ | 52% ~28s
|+++++++++++++++++++++++++++ | 53% ~27s
|++++++++++++++++++++++++++++ | 54% ~27s
|++++++++++++++++++++++++++++ | 56% ~26s
|+++++++++++++++++++++++++++++ | 57% ~25s
|++++++++++++++++++++++++++++++ | 58% ~24s
|++++++++++++++++++++++++++++++ | 59% ~24s
|+++++++++++++++++++++++++++++++ | 61% ~23s
|++++++++++++++++++++++++++++++++ | 62% ~22s
|++++++++++++++++++++++++++++++++ | 63% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|+++++++++++++++++++++++++++++++++ | 66% ~20s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|+++++++++++++++++++++++++++++++++++ | 68% ~19s
|+++++++++++++++++++++++++++++++++++ | 70% ~18s
|++++++++++++++++++++++++++++++++++++ | 71% ~17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~17s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~15s
|++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 59s
| | 0 % ~calculating
|+ | 1 % ~01m 27s
|++ | 2 % ~01m 25s
|++ | 3 % ~01m 31s
|+++ | 4 % ~01m 29s
|+++ | 6 % ~01m 26s
|++++ | 7 % ~01m 24s
|++++ | 8 % ~01m 23s
|+++++ | 9 % ~01m 21s
|+++++ | 10% ~01m 20s
|++++++ | 11% ~01m 18s
|+++++++ | 12% ~01m 17s
|+++++++ | 13% ~01m 16s
|++++++++ | 14% ~01m 15s
|++++++++ | 16% ~01m 14s
|+++++++++ | 17% ~01m 13s
|+++++++++ | 18% ~01m 11s
|++++++++++ | 19% ~01m 10s
|++++++++++ | 20% ~01m 09s
|+++++++++++ | 21% ~01m 08s
|++++++++++++ | 22% ~01m 07s
|++++++++++++ | 23% ~01m 06s
|+++++++++++++ | 24% ~01m 05s
|+++++++++++++ | 26% ~01m 04s
|++++++++++++++ | 27% ~01m 03s
|++++++++++++++ | 28% ~01m 02s
|+++++++++++++++ | 29% ~01m 01s
|+++++++++++++++ | 30% ~01m 01s
|++++++++++++++++ | 31% ~60s
|+++++++++++++++++ | 32% ~59s
|+++++++++++++++++ | 33% ~58s
|++++++++++++++++++ | 34% ~57s
|++++++++++++++++++ | 36% ~56s
|+++++++++++++++++++ | 37% ~55s
|+++++++++++++++++++ | 38% ~54s
|++++++++++++++++++++ | 39% ~53s
|++++++++++++++++++++ | 40% ~52s
|+++++++++++++++++++++ | 41% ~51s
|++++++++++++++++++++++ | 42% ~50s
|++++++++++++++++++++++ | 43% ~49s
|+++++++++++++++++++++++ | 44% ~48s
|+++++++++++++++++++++++ | 46% ~47s
|++++++++++++++++++++++++ | 47% ~46s
|++++++++++++++++++++++++ | 48% ~45s
|+++++++++++++++++++++++++ | 49% ~44s
|+++++++++++++++++++++++++ | 50% ~43s
|++++++++++++++++++++++++++ | 51% ~42s
|+++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|++++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|+++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~33s
|++++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~31s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 66% ~30s
|++++++++++++++++++++++++++++++++++ | 67% ~29s
|++++++++++++++++++++++++++++++++++ | 68% ~28s
|+++++++++++++++++++++++++++++++++++ | 69% ~27s
|+++++++++++++++++++++++++++++++++++ | 70% ~26s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 27s
| | 0 % ~calculating
|+ | 1 % ~01m 11s
|++ | 2 % ~01m 12s
|++ | 3 % ~01m 11s
|+++ | 4 % ~01m 12s
|+++ | 5 % ~01m 12s
|++++ | 6 % ~01m 12s
|++++ | 7 % ~01m 11s
|+++++ | 8 % ~01m 10s
|+++++ | 9 % ~01m 09s
|++++++ | 10% ~01m 08s
|++++++ | 11% ~01m 08s
|+++++++ | 12% ~01m 07s
|+++++++ | 13% ~01m 06s
|++++++++ | 14% ~01m 05s
|++++++++ | 15% ~01m 04s
|+++++++++ | 16% ~01m 04s
|+++++++++ | 17% ~01m 03s
|++++++++++ | 18% ~01m 02s
|++++++++++ | 19% ~01m 01s
|+++++++++++ | 20% ~01m 01s
|+++++++++++ | 21% ~60s
|++++++++++++ | 22% ~59s
|++++++++++++ | 23% ~58s
|+++++++++++++ | 24% ~58s
|+++++++++++++ | 25% ~57s
|++++++++++++++ | 26% ~56s
|++++++++++++++ | 27% ~55s
|+++++++++++++++ | 28% ~55s
|+++++++++++++++ | 29% ~54s
|++++++++++++++++ | 30% ~54s
|++++++++++++++++ | 31% ~53s
|+++++++++++++++++ | 32% ~52s
|+++++++++++++++++ | 33% ~52s
|++++++++++++++++++ | 34% ~51s
|++++++++++++++++++ | 35% ~50s
|+++++++++++++++++++ | 36% ~49s
|+++++++++++++++++++ | 37% ~48s
|++++++++++++++++++++ | 38% ~48s
|++++++++++++++++++++ | 39% ~47s
|+++++++++++++++++++++ | 40% ~46s
|+++++++++++++++++++++ | 41% ~45s
|++++++++++++++++++++++ | 42% ~44s
|++++++++++++++++++++++ | 43% ~44s
|+++++++++++++++++++++++ | 44% ~43s
|+++++++++++++++++++++++ | 45% ~42s
|++++++++++++++++++++++++ | 46% ~41s
|++++++++++++++++++++++++ | 47% ~40s
|+++++++++++++++++++++++++ | 48% ~39s
|+++++++++++++++++++++++++ | 49% ~39s
|++++++++++++++++++++++++++ | 51% ~38s
|++++++++++++++++++++++++++ | 52% ~37s
|+++++++++++++++++++++++++++ | 53% ~36s
|+++++++++++++++++++++++++++ | 54% ~35s
|++++++++++++++++++++++++++++ | 55% ~35s
|++++++++++++++++++++++++++++ | 56% ~34s
|+++++++++++++++++++++++++++++ | 57% ~33s
|+++++++++++++++++++++++++++++ | 58% ~32s
|++++++++++++++++++++++++++++++ | 59% ~32s
|++++++++++++++++++++++++++++++ | 60% ~31s
|+++++++++++++++++++++++++++++++ | 61% ~30s
|+++++++++++++++++++++++++++++++ | 62% ~29s
|++++++++++++++++++++++++++++++++ | 63% ~28s
|++++++++++++++++++++++++++++++++ | 64% ~28s
|+++++++++++++++++++++++++++++++++ | 65% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~24s
|+++++++++++++++++++++++++++++++++++ | 70% ~23s
|++++++++++++++++++++++++++++++++++++ | 71% ~22s
|++++++++++++++++++++++++++++++++++++ | 72% ~21s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 16s
| | 0 % ~calculating
|+ | 1 % ~03m 49s
|++ | 2 % ~03m 41s
|++ | 3 % ~03m 38s
|+++ | 4 % ~03m 30s
|+++ | 5 % ~03m 26s
|++++ | 6 % ~03m 22s
|++++ | 8 % ~03m 18s
|+++++ | 9 % ~03m 18s
|+++++ | 10% ~03m 15s
|++++++ | 11% ~03m 13s
|++++++ | 12% ~03m 11s
|+++++++ | 13% ~03m 08s
|+++++++ | 14% ~03m 05s
|++++++++ | 15% ~03m 02s
|+++++++++ | 16% ~02m 59s
|+++++++++ | 17% ~02m 57s
|++++++++++ | 18% ~02m 54s
|++++++++++ | 19% ~02m 52s
|+++++++++++ | 20% ~02m 49s
|+++++++++++ | 22% ~02m 46s
|++++++++++++ | 23% ~02m 44s
|++++++++++++ | 24% ~02m 41s
|+++++++++++++ | 25% ~02m 39s
|+++++++++++++ | 26% ~02m 36s
|++++++++++++++ | 27% ~02m 35s
|++++++++++++++ | 28% ~02m 32s
|+++++++++++++++ | 29% ~02m 30s
|++++++++++++++++ | 30% ~02m 28s
|++++++++++++++++ | 31% ~02m 26s
|+++++++++++++++++ | 32% ~02m 24s
|+++++++++++++++++ | 33% ~02m 21s
|++++++++++++++++++ | 34% ~02m 19s
|++++++++++++++++++ | 35% ~02m 16s
|+++++++++++++++++++ | 37% ~02m 14s
|+++++++++++++++++++ | 38% ~02m 12s
|++++++++++++++++++++ | 39% ~02m 09s
|++++++++++++++++++++ | 40% ~02m 07s
|+++++++++++++++++++++ | 41% ~02m 05s
|+++++++++++++++++++++ | 42% ~02m 03s
|++++++++++++++++++++++ | 43% ~02m 00s
|+++++++++++++++++++++++ | 44% ~01m 58s
|+++++++++++++++++++++++ | 45% ~01m 56s
|++++++++++++++++++++++++ | 46% ~01m 53s
|++++++++++++++++++++++++ | 47% ~01m 51s
|+++++++++++++++++++++++++ | 48% ~01m 49s
|+++++++++++++++++++++++++ | 49% ~01m 46s
|++++++++++++++++++++++++++ | 51% ~01m 44s
|++++++++++++++++++++++++++ | 52% ~01m 42s
|+++++++++++++++++++++++++++ | 53% ~01m 39s
|+++++++++++++++++++++++++++ | 54% ~01m 37s
|++++++++++++++++++++++++++++ | 55% ~01m 35s
|++++++++++++++++++++++++++++ | 56% ~01m 32s
|+++++++++++++++++++++++++++++ | 57% ~01m 31s
|++++++++++++++++++++++++++++++ | 58% ~01m 29s
|++++++++++++++++++++++++++++++ | 59% ~01m 26s
|+++++++++++++++++++++++++++++++ | 60% ~01m 24s
|+++++++++++++++++++++++++++++++ | 61% ~01m 22s
|++++++++++++++++++++++++++++++++ | 62% ~01m 19s
|++++++++++++++++++++++++++++++++ | 63% ~01m 17s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 15s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 13s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 10s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 08s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 06s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 04s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~59s
|+++++++++++++++++++++++++++++++++++++ | 73% ~57s
|++++++++++++++++++++++++++++++++++++++ | 74% ~54s
|++++++++++++++++++++++++++++++++++++++ | 75% ~52s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~50s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~48s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~45s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~43s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~41s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~39s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~37s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~35s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~33s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~30s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~23s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~21s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 33s
| | 0 % ~calculating
|+ | 1 % ~06m 60s
|+ | 2 % ~06m 44s
|++ | 3 % ~06m 36s
|++ | 4 % ~06m 34s
|+++ | 5 % ~06m 29s
|+++ | 6 % ~06m 22s
|++++ | 7 % ~06m 21s
|++++ | 8 % ~06m 16s
|+++++ | 9 % ~06m 10s
|+++++ | 10% ~06m 04s
|++++++ | 11% ~05m 59s
|++++++ | 12% ~05m 53s
|+++++++ | 13% ~05m 48s
|+++++++ | 14% ~05m 44s
|++++++++ | 15% ~05m 39s
|++++++++ | 16% ~05m 35s
|+++++++++ | 17% ~05m 30s
|+++++++++ | 18% ~05m 26s
|++++++++++ | 19% ~05m 22s
|++++++++++ | 20% ~05m 19s
|+++++++++++ | 21% ~05m 15s
|+++++++++++ | 22% ~05m 12s
|++++++++++++ | 23% ~05m 11s
|++++++++++++ | 24% ~05m 07s
|+++++++++++++ | 25% ~05m 03s
|+++++++++++++ | 26% ~04m 60s
|++++++++++++++ | 27% ~04m 56s
|++++++++++++++ | 28% ~04m 52s
|+++++++++++++++ | 29% ~04m 48s
|+++++++++++++++ | 30% ~04m 43s
|++++++++++++++++ | 31% ~04m 38s
|++++++++++++++++ | 32% ~04m 34s
|+++++++++++++++++ | 33% ~04m 29s
|+++++++++++++++++ | 34% ~04m 25s
|++++++++++++++++++ | 35% ~04m 21s
|++++++++++++++++++ | 36% ~04m 17s
|+++++++++++++++++++ | 37% ~04m 13s
|+++++++++++++++++++ | 38% ~04m 08s
|++++++++++++++++++++ | 39% ~04m 04s
|++++++++++++++++++++ | 40% ~03m 60s
|+++++++++++++++++++++ | 41% ~03m 57s
|+++++++++++++++++++++ | 42% ~03m 53s
|++++++++++++++++++++++ | 43% ~03m 48s
|++++++++++++++++++++++ | 44% ~03m 44s
|+++++++++++++++++++++++ | 45% ~03m 40s
|+++++++++++++++++++++++ | 46% ~03m 36s
|++++++++++++++++++++++++ | 47% ~03m 32s
|++++++++++++++++++++++++ | 48% ~03m 28s
|+++++++++++++++++++++++++ | 49% ~03m 24s
|+++++++++++++++++++++++++ | 50% ~03m 19s
|++++++++++++++++++++++++++ | 51% ~03m 15s
|++++++++++++++++++++++++++ | 52% ~03m 11s
|+++++++++++++++++++++++++++ | 53% ~03m 07s
|+++++++++++++++++++++++++++ | 54% ~03m 03s
|++++++++++++++++++++++++++++ | 55% ~02m 59s
|++++++++++++++++++++++++++++ | 56% ~02m 55s
|+++++++++++++++++++++++++++++ | 57% ~02m 51s
|+++++++++++++++++++++++++++++ | 58% ~02m 47s
|++++++++++++++++++++++++++++++ | 59% ~02m 43s
|++++++++++++++++++++++++++++++ | 60% ~02m 39s
|+++++++++++++++++++++++++++++++ | 61% ~02m 35s
|+++++++++++++++++++++++++++++++ | 62% ~02m 31s
|++++++++++++++++++++++++++++++++ | 63% ~02m 27s
|++++++++++++++++++++++++++++++++ | 64% ~02m 23s
|+++++++++++++++++++++++++++++++++ | 65% ~02m 19s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 15s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 11s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 07s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 03s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 60s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 56s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 52s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 47s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 43s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 39s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 35s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 31s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 27s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 19s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 07s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01m 03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~59s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~55s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~51s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~48s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~44s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~40s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~36s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~28s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~24s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06m 34sDisplay the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.
Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types: Cluster 0: basal cell of epidermis (CL:0002187) Cluster 1: differentiated keratinocytes (CL:0000312) Cluster 2: proliferating basal cell of epidermis (CL:0002187) Clutser 3: suprabasal differentiating keratinocytes (CL:0000312) Cluster 4: basal cell of epidermis (CL:0002187) Cluster 5: suprabasal differentiated keratinocytes (CL:0000312) Cluster 6: basal cell of epidermis (CL:0002187) Cluster 7: basal cell of epidermis (CL:0002187) Clutser 8: basal cell of epidermis (CL:0002187) Cluster 9: filiform differentiated keratinocytes (CL:0000312) Cluster 10: Langerhans cells (CL_0000453)
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
free_cell_ontology_id <- c("basal_cells", "differentiated", "proliferating","suprabasal_differentiating", "basal_cells","suprabasal_differentiated", "basal_cells", "basal_cells", "basal_cells", "filiform_differentiated", "Langerhans cells")
cell_ontology_class <- c("CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0002187", "CL:0002187", "CL:0000312", 'CL_0000453')
cell_ontology_id <- c("CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0002187", "CL:0002187", "CL:0000312s", 'CL_0000453')
#subcell_ontology_class = c("differentiating", "differentiating", "differentiating", "proliferating", "differentiating", "differentiating", "differentiating", "differentiating", "Filiform", NA)
cell_ontology_class.subcell_ontology_class = paste0(cell_ontology_class,': ', free_cell_ontology_id)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
tiss@meta.data[,'subcell_ontology_class'] <- plyr::mapvalues(x = tiss@meta.data$cluster.ids, from = cluster.ids, to = free_cell_ontology_id)
tiss@meta.data[,'cell_ontology_class.subcell_ontology_class'] <- plyr::mapvalues(x = tiss@meta.data$cluster.ids, from = cluster.ids, to = cell_ontology_class.subcell_ontology_class)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class.subcell_ontology_class')
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$channel))
10X_P4_0 10X_P4_1 10X_P7_10
0 676 677 300
1 640 316 421
10 16 9 12
2 206 233 414
3 383 258 139
4 292 275 131
5 232 249 108
6 155 224 110
7 259 137 75
8 162 33 194
9 80 77 45Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/Jintonic/Documents/GitHub/tabula-muris/00_data_ingest/04_tissue_robj_generated/dropletTongue_seurat_tiss.Robj"save(tiss, file=filename)# To reload a saved object
# filename = here('00_data_ingest', '04_tissue_robj_generated',
# paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', '03_tissue_annotation_csv',
paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_class.subcell_ontology_class')], file=filename)---
 title: "Tongue Droplet Notebook"
 output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Tongue"
```

Load the requisite packages and some additional helper functions.

```{r}
library(here)
library(useful)
library(Seurat)
library(dplyr)
library(Matrix)
library(ontologyIndex)
cell_ontology = get_ontology('https://raw.githubusercontent.com/obophenotype/cell-ontology/master/cl-basic.obo', extract_tags='everything')

validate_cell_ontology = function(cell_ontology_class){
  in_cell_ontology = sapply(cell_ontology_class, function(x) is.element(x, cell_ontology$name))
  if (!all(in_cell_ontology)) {
    message = paste0('"', cell_ontology_class[!in_cell_ontology], '" is not in the cell ontology
')
    stop(message)
  }
}
convert_to_cell_ontology_id = function(cell_ontology_class){
  return(sapply(cell_ontology_class, function(x) as.vector(cell_ontology$id[cell_ontology$name == x])[1]))
}
save_dir = here('00_data_ingest', '04_tissue_robj_generated')
```



```{r}
# read the metadata to get the plates we want
droplet_metadata_filename = here('00_data_ingest', '01_droplet_raw_data', 'metadata_droplet.csv')

droplet_metadata <- read.csv(droplet_metadata_filename, sep=",", header = TRUE)
colnames(droplet_metadata)[1] <- "channel"
droplet_metadata
```

Subset the metadata on the tissue.

```{r}
tissue_metadata = filter(droplet_metadata, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex')]
tissue_metadata
```


Use only the metadata rows corresponding to Bladder plates. Make a plate barcode dataframe to "expand" the per-plate metadata to be per-cell.

```{r}
# Load the gene names and set the metadata columns by opening the first file

subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[1])
raw.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata$channel[1], '_', x))
meta.data = data.frame(row.names = colnames(raw.data))
meta.data['channel'] = tissue_metadata$channel[1]

if (length(tissue_metadata$channel) > 1){
  # Some tissues, like Thymus and Heart had only one channel
  for(i in 2:nrow(tissue_metadata)){
    subfolder = paste0(tissue_of_interest, '-', tissue_metadata$channel[i])
    new.data <- Read10X(data.dir = here('00_data_ingest', '01_droplet_raw_data', 'droplet', subfolder))
    colnames(new.data) <- lapply(colnames(new.data), function(x) paste0(tissue_metadata$channel[i], '_', x))
    
    new.metadata = data.frame(row.names = colnames(new.data))
    new.metadata['channel'] = tissue_metadata$channel[i]
    
    raw.data = cbind(raw.data, new.data)
    meta.data = rbind(meta.data, new.metadata)
  }
}

rnames = row.names(meta.data)
meta.data <- merge(meta.data, tissue_metadata, sort = F)
row.names(meta.data) <- rnames
dim(raw.data)
corner(raw.data)
head(meta.data)
```
Process the raw data and load it into the Seurat object.

```{r}
# Find ERCC's, compute the percent ERCC, and drop them from the raw data.
erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
raw.data <- raw.data[-ercc.index,]

# Create the Seurat object with all the data
tiss <- CreateSeuratObject(raw.data = raw.data, project = tissue_of_interest, 
                    min.cells = 5, min.genes = 5)

tiss <- AddMetaData(object = tiss, meta.data)
tiss <- AddMetaData(object = tiss, percent.ercc, col.name = "percent.ercc")
# Change default name for sums of counts from nUMI to nReads
# colnames(tiss@meta.data)[colnames(tiss@meta.data) == 'nUMI'] <- 'nReads'

# Create metadata columns for cell_ontology_classs and subcell_ontology_classs
tiss@meta.data[,'cell_ontology_class'] <- NA
tiss@meta.data[,'subcell_ontology_class'] <- NA
```


Calculate percent ribosomal genes.

```{r}
ribo.genes <- grep(pattern = "^Rp[sl][[:digit:]]", x = rownames(x = tiss@data), value = TRUE)
percent.ribo <- Matrix::colSums(tiss@raw.data[ribo.genes, ])/Matrix::colSums(tiss@raw.data)
tiss <- AddMetaData(object = tiss, metadata = percent.ribo, col.name = "percent.ribo")
```

A sanity check: genes per cell vs reads per cell.

```{r}
GenePlot(object = tiss, gene1 = "nUMI", gene2 = "nGene", use.raw=T)
```

Filter out cells with few reads and few genes.

```{r}
tiss <- FilterCells(object = tiss, subset.names = c("nGene", "nUMI"), 
    low.thresholds = c(500, 1000), high.thresholds = c(25000, 5000000))
```


Normalize the data, then regress out correlation with total reads
```{r}
tiss <- NormalizeData(object = tiss, scale.factor = 1e4)
tiss <- ScaleData(object = tiss)
tiss <- FindVariableGenes(object = tiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5)
```


Run Principal Component Analysis.
```{r}
tiss <- RunPCA(object = tiss, do.print = FALSE)
tiss <- ProjectPCA(object = tiss, do.print = FALSE)
```

```{r, echo=FALSE, fig.height=16, fig.width=8}
#PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
PCHeatmap(object = tiss, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, 
    label.columns = FALSE, use.full = FALSE)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 14
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```


To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=35, dim.embed = 2)
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
		  paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

Check expression of genes of interset.

```{r, echo=FALSE, fig.height=16, fig.width=10}
genes_to_check = c('Itgb4', 'Itga6', 'Itgb1', 'Cdh3', 'Top2a', 'Mt2', 'Krt15', 'Krt5', 'Krt14', 'Sbsn', 'Krt10','Klf4'
, 'Krt84', 'Krt36','Krt33b', 'Hoxc13', 'Krt4', 'Krt13', 'Fabp5', 'Krt32',  'Krt78', 'Vim', 'Hhip', 'Ptch1', 'Gli1','Dsc1', 'Dcn', 'Runx2', 'Sox2', 'Pax9')
FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 4)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```


```{r, echo=FALSE, fig.height=20, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check, y.log = TRUE)
```


How big are the clusters?
```{r}
table(tiss@ident)
```

```{r}
tiss1=BuildClusterTree(tiss)
```

```{r}
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster7.markers <- FindMarkers(object = tiss, ident.1 = 7, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster8.markers <- FindMarkers(object = tiss, ident.1 = 8, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster9.markers <- FindMarkers(object = tiss, ident.1 = 9, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
```

Which markers identify a specific cluster?
```{r}
print(x = head(x = cluster6.markers, n = 30))
```


You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```


Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.
```{r, echo=FALSE, fig.height=50, fig.width=35}
#top10 <- tiss.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top20 <- tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
DoHeatmap(tiss, genes.use = top20$gene, slim.col.label = TRUE, remove.key = TRUE, cex.row = 20, group.cex = 20 )
```



## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
Cluster 0: basal cell of epidermis (CL:0002187)
Cluster 1: differentiated keratinocytes (CL:0000312)
Cluster 2: proliferating basal cell of epidermis (CL:0002187)
Clutser 3: suprabasal differentiating keratinocytes  (CL:0000312)
Cluster 4: basal cell of epidermis (CL:0002187)
Cluster 5: suprabasal differentiated keratinocytes (CL:0000312)
Cluster 6: basal cell of epidermis (CL:0002187)
Cluster 7: basal cell of epidermis (CL:0002187)
Clutser 8: basal cell of epidermis (CL:0002187)
Cluster 9: filiform differentiated keratinocytes (CL:0000312)
Cluster 10: Langerhans cells (CL_0000453)
```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
free_cell_ontology_id <- c("basal_cells", "differentiated", "proliferating","suprabasal_differentiating", "basal_cells","suprabasal_differentiated", "basal_cells", "basal_cells", "basal_cells", "filiform_differentiated", "Langerhans cells")
cell_ontology_class <- c("CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0002187", "CL:0002187", "CL:0000312", 'CL_0000453')
cell_ontology_id <- c("CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0000312", "CL:0002187", "CL:0002187", "CL:0002187", "CL:0000312s", 'CL_0000453')

#subcell_ontology_class = c("differentiating", "differentiating", "differentiating", "proliferating", "differentiating", "differentiating", "differentiating", "differentiating", "Filiform", NA)

cell_ontology_class.subcell_ontology_class = paste0(cell_ontology_class,': ', free_cell_ontology_id)

tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

tiss@meta.data[,'subcell_ontology_class'] <- plyr::mapvalues(x = tiss@meta.data$cluster.ids, from = cluster.ids, to = free_cell_ontology_id)
tiss@meta.data[,'cell_ontology_class.subcell_ontology_class'] <- plyr::mapvalues(x = tiss@meta.data$cluster.ids, from = cluster.ids, to = cell_ontology_class.subcell_ontology_class)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class.subcell_ontology_class')
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")
```

```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$channel))
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
		  paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
# filename = here('00_data_ingest', '04_tissue_robj_generated', 
#                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
# load(file=filename)
```



# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_annotation_csv', 
		  paste0(tissue_of_interest, "_droplet_cell_ontology_class.csv"))
write.csv(tiss@meta.data[,c('channel','cell_ontology_class','cell_ontology_class.subcell_ontology_class')], file=filename)
```
